Calculating Enzyme Inhibition with Random Forests
Amauri Duarte da Silva1, Walter Filgueira de Azevedo2
1Graduate Program in Information Technologies and Health Management, Federal University of Health Sciences of Porto Alegre, Porto Alegre, RS, Brazil.
Abstract:
Random forest is an advanced supervised machine learning method to build models for complex systems. This technique belongs to the ensemble method class, one of the most high-powered approaches in artificial intelligence. The flexibility of this technique allows its application to classification and regression tasks. Here, we aim to build a regression model to calculate binding affinity based on docked structures determined with the program Molegro Virtual Docker. A Google Colab workflow permits us to employ the docking results to generate regression models. Our code relies on the Random Forest method available in the Scikit-Learn library. We built a Random Forest model to predict the inhibition of cyclin-dependent kinase 2. This enzyme participates in the control of cell cycle progression and is a target for anticancer drugs. All datasets and Jupyter Notebooks with MVD4ML and SKReg4Model discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme . We made the program SAnDReS 2.0 available at https://github.com/azevedolab/sandres .
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